End of (this) trail
Updated: Jan 19
This week I am finishing a 520 hour Data Science Intensive program with General Assembly. It's been a bit of a ride. 40 hours per week in class, 10-20 hours a week of lab, background research, and practice.
When I started this course I had a decent understanding of probability distributions and a background in web development programming. My time learning ruby and ruby on rails had only shown me I was uninterested in web development.
This time, this program, has only confirmed that I enjoy the data science process and can't wait to try new and different projects in the field. A few of the projects I have my eye on are:
Real time sound classification
The Urbansounds 8K dataset is commonly used to identify background noises and is often used as a practice dataset for realtime sound identification. Given that I may have jumped into the deep end of sound identification when I started working with SER, this might be a good way to step back and solidify the basics of sound transformations. Plus, I might be able to hang a microphone out my window and practice on the construction noise.
Tax Data Classifier
I still have that nonprofit tax dataset, and while I may not have a particularly useful model to build right now, I can at least use it to practice applications of Benford's law and check for statistical anomalies. Perhaps I can build an audit risk model or a 'politically active' classifier. It might also be a good dataset to practice working with Tableau in.
I'm grateful for my time at GA and what I've learned. Four months ago I never would have thought I could come this far, I would have figured that Data Science was a far away horizon, a north star I could keep working towards and maybe someday if I was good enough I could reach the starting line. Instead, I found that I've been on this path all along, I simply needed some more tools that I could learn and someone in the field who could tell me where the trailhead was. The constant in my life and in the roles I've worked in since college has been information, data, and I've always sought more ways to leverage the information we had available to make an improvement in people's lives.
I'm looking forward to the next steps.